AI Engineer (Mid-Level)
At a glance
Mid-level AI Engineering role at Clera. San Francisco · full-time · $180,000–$400,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build and maintain agents that handle complicated, multi-step work in regulated settings
- Run RAG services and retrieval foundations, including vector stores, embeddings and domain-specific indexing
- Create agent coordination, tool use, memory and reasoning features for dependable AI products
- Set up evaluations and safeguards to track model quality, catch regressions and support enterprise reliability
- Deliver AI features across APIs, data models, frontend, backend and production operations, then refine them with user feedback
What you bring
- You have two to eight years in software engineering and have shipped products for users or backend customers
- You have deployed LLM services in production, including prompt work, orchestration and connecting tools
- You can work across Python, TypeScript or React, cloud services and relational or NoSQL data stores
- You understand RAG, vector databases, embeddings and retrieval, and can choose where those methods fit
- You have built tests, evaluations and monitoring for AI systems, plus API-based systems designed for high throughput or real-time use
Who this fits
This mid-level role suits an engineer who has shipped production AI services and can contribute across the stack. It is based onsite in San Francisco.
From the employer
About the Role
Join a fast-moving, pre-seed-backed AI startup building the next generation of agentic systems that automate complex, multi-step workflows across regulated and enterprise domains — including healthcare, legal, fintech, logistics, and compliance. As a mid-level AI Engineer on the core product team, you'll own production LLM-based services end-to-end, collaborate closely with founders and product, and ship features that deliver measurable impact for real users.
What You'll Do
Design, build, and maintain agentic systems that automate complex, multi-step workflows across regulated industries.
Own production retrieval-augmented generation (RAG) pipelines and retrieval infrastructure, including vector databases, embeddings, and indexing for domain-specific search at scale.
Implement multi-agent orchestration, tool-calling, memory, and reasoning components to deliver robust AI-driven user experiences.
Develop evaluation and safety infrastructure to measure model performance, surface regressions, and enforce enterprise-level trust and reliability.
Ship full-stack AI products from MVP to enterprise-grade — designing APIs and data models, writing frontend and backend code, and operating production systems with CI/CD, monitoring, and testing.
Collaborate with founders, product, and design to prioritize work, define success metrics, and iterate based on user feedback and telemetry.
What We're Looking For
Must-haves:
2–8 years of software engineering experience with demonstrated delivery of shipped user-facing or backend products.
Practical experience deploying LLMs or LLM-based services in production, including prompt design, orchestration, and tool integration.
Proficiency across the stack: Python plus TypeScript/React (or equivalent), cloud platforms (AWS or GCP), and relational or NoSQL databases.
Working knowledge of RAG patterns, vector databases, embeddings, and retrieval pipelines, with sound judgment on when to apply each approach.
Experience building automated tests, evaluations, and monitoring for AI systems to ensure reliability beyond demos.
Experience designing API-driven, high-throughput systems and real-time product features.
Nice-to-haves:
Experience with agent or workflow frameworks (e.g., LangGraph, CrewAI) and orchestration tools (e.g., Temporal, Trigger).
Familiarity with fine-tuning, parameter-efficient tuning, or multi-modal model integration.
Background building multi-tenant or enterprise-ready systems, or prior experience in regulated industries such as healthcare, fintech, or legal.
Compensation & Benefits
Salary: $180,000 – $400,000 USD annually (reflecting a wide band across base and equity depending on experience).
Early-stage equity in a venture-backed startup.